Astragalus L. is one of the largest angiosperm complex genera that belongs to the family Fabaceae, subfamily Papilionoideae or Faboideae under the subtribe Astragalinae of the tribe Galegeae. The current study includes the whole plant morphology, DNA barcode (ITS2), and molecular marker (SCoT). Ten taxa representing four species of Astragalus were collected from different localities in Egypt during the period from February 2018 to May 2019. Morphologically, identification and classification of collected Astragalus plants occurred by utilizing the light microscope, regarding the taxonomic revisions of the reference collected Astragalus specimens in other Egyptian Herbaria. For molecular validation, ten SCoT primers were used in this study, producing a unique banding pattern to differentiate between ten samples of Astragalus taxa which generated 212 DNA fragments with an average of 12.2 bands per 10 Astragalus samples, with 8 to 37 fragments per primer. The 212 fragments amplified were distributed as 2 monomorphic bands, 27 polymorphic without unique bands, 183 unique bands (210 Polymorphic with unique bands), and ITS2 gene sequence was showed as the optimal barcode for identifying Astragalus L. using BLAST searched on NCBI database, and afterward, analyzing the chromatogram for ITS region, 10 samples have been identified as two samples representing A. hauarensis, four samples representing A. sieberi, three samples representing A. spinosus and one sample representing A. vogelii. Based on the ITS barcode, A. hauarensis RMG1, A. hauarensis RMG2, A. sieberi RMG1, A. sieberi RMG2, A. sieberi RMG3, A. sieberi RMG4, A. spinosus RMG1, A. spinosus RMG2, A. spinosus RMG3, A. vogelii RMG were deposited into GenBank with accession # MT367587.1, MT367591.1, MT367593.1, MT367585.1, MT367586.1, MT367588.1, MT160347.1, MT367590.1, MT367589.1, MT367592.1, respectively. These results indicated the efficiency of SCoT markers and ITS2 region in identifying and determining genetic relationships between Astragalus species.

Photovoltaic (PV) power is most commonly used for water pumping applications. The DC output voltage of PV arrays is connected to a DC-DC converter using a maximum power point tracking (MPPT) controller to maximize their produced energy. Then, that converter is linked to a voltage source inverter (VSI) that converts DC power to AC power. Vector control is used to control the VSI fed three phase induction motor driving the water pump. The Affinity laws are used to change the pump characteristics by changing the pump speed, and consequently, the pump flow rate, head, and power will be varied. In this paper, the Affinity laws are adapted to achieve the pump hydraulic requirements while the power delivered to the pump motor remains unchanged by constructing new pump curves. A Matlab/Simulink model of the PV pumping system is observed over a wide range of weather and loading conditions.

The photovoltaic (PV) solar electricity is no longer doubtful in its effectiveness in the process of rural communities’ livelihood transformation with solar water pumping system being regarded as the most important PV application. To overcome the intermittent and uncertain nature of solar power output, the highly fluctuating load demands and to supply loads at night time, a battery storage system is optimally sized, designed and implemented. The bi-directional Buck-Boost converter use and control are essential for energy management between the batteries and the pumping system. Domestic loads power calculation is also demonstrated and varied. Additionally, various inverter control schemes are examined and employed depending on the nature of the load connected. Finally, simulation results using Matlab/Simulink are presented for two cases: when the battery system is connected with the PV array to feed the pump motor to achieve the required varying hydraulic performance (flow rate and pumping head) under different weather conditions, and when the battery system feeds the loads while the PV array is disconnected at night.

In rural areas which are located far from the electrical grid, renewable energy systems such as photovoltaic (PV) energy are investigated. The most popular PV application is solar water pumping for irrigation. DC-DC converter and maximum power point tracking are used because the PV modules output varies widely due to varying weather conditions. The water pump is driven by a three phase induction motor through a voltage source inverter (VSI). However, the control of induction motor is known to be difficult because it's highly non-linear and time variant. One method to mitigate this is by using vector control techniques to control the VSI as they offer a number of benefits including speed control and regulation over a wide range and fast dynamic response. The proportional - integral (PI) controller is most commonly used in the speed control loop of vector control. This paper deals with the design of the speed PI controller parameters (gains) using particle swarm optimization (PSO) technique and compares it with the conventional Ziegler-Nichols (ZN) method. Different objective functions have been proposed which are used to evaluate the optimization algorithm. The optimum solution mainly converges to a minimum error which affects the control parameters such as the maximum overshoot, rise time and settling time of the system. Simulation results are obtained using Matlab/Simulink program for photovoltaic pump application during load variation (pump head and flow rate variation). The results show the advantage of the PSO-based optimization approach.

Background: Stroke is one of the most common and fatal neurologic abnormalities. Multiple risk factors, including smoking, hypertension, diabetes and hyperlipidemia, can lead to a stroke. This study aimed to estimate the stroke short-term risk and its associated factors among SARS-CoV-2 hospitalized cases. Methods: This cross-sectional comparison groups was conducted on SARS-CoV-2 infected patients in two phases: Phase 1: cross sectional study in which the prevalence of stroke could be estimated. Phase 2: grouping among the study population in order to find out different risk factors. All patients underwent medical history taking, full general and neurological examinations, PCR or chest CT scan screening, brain CT scan and Canadian Neurological Scale Results: There was no significant correlation between the stroke different types and risk factors. There was an insignificant correlation between stroke severity and risk factors. There was no statistically significant difference in stroke severity; assessed by Canadian neurological scale; between different stroke types. Conclusion: We detected a low incidence of imaging-confirmed ischemic stroke in hospitalized COVID-19-infected individuals. Mild, Moderate, and Severe on the Canadian neurological scale were unrelated to the observed results (AIS, ICH, and CVT).